CN-122026771-A - Rotary motor operation support system and method thereof

CN122026771ACN 122026771 ACN122026771 ACN 122026771ACN-122026771-A

Abstract

The invention relates to the technical field of fault prediction and health management of rotating motors, in particular to a rotating motor operation support system and a rotating motor operation support method, wherein the rotating motor operation support system comprises the steps of collecting operation data of the rotating motor, and extracting harmonic voltage and harmonic current based on the operation data; the method comprises the steps of calculating an electrical stress index and a mechanical stress index, generating systematic latent stress, calculating an evolution rate of the systematic latent stress, generating a dynamic failure boundary based on the electrical stress index, calculating a mode switching decision function value according to the systematic latent stress, the evolution rate and the dynamic failure boundary, generating a control mode switching decision based on the function value, responding to the control mode switching decision, generating and executing a suppression control strategy, collecting operation data after executing the suppression control strategy, and modifying the suppression control strategy in a closed loop mode based on the updated operation data.

Inventors

YU LILI
GUO JIONGXIAN
GUO BINGXUN
YANG YONGYU

Assignees

淮安仲益电机有限公司

Dates

Publication Date: 20260512
Application Date: 20260414

Claims (8)

1. A rotary electric machine operation support method characterized by comprising the steps of: s1, collecting operation data of a rotating motor, wherein the operation data comprise three-phase current, terminal voltage and mechanical vibration acceleration; S2, calculating an electrical stress index and a mechanical stress index, wherein the electrical stress index is determined based on harmonic voltage and harmonic current, and the mechanical stress index is determined based on mechanical vibration acceleration; S3, calculating the evolution rate of systematic latent stress, and generating a dynamic failure boundary based on an electrical stress index; s4, calculating a mode switching decision function value according to the systematic latent stress, the evolution rate and the dynamic failure boundary, and generating a control mode switching decision based on the function value; s5, generating and executing a suppression control strategy in response to the control mode switching decision; s6, collecting the operation data after executing the inhibitory control strategy, and correcting the inhibitory control strategy in a closed loop based on the updated operation data.
2. The rotary electric machine operation support method according to claim 1, wherein S2 specifically includes: S21, comparing the monitored subsynchronous harmonic power with the rated power of the motor, and carrying out normalization calculation to obtain an electrical stress index; s22, comparing the accumulated vibration energy in a period of time with a preset reference energy value, and carrying out normalization calculation to obtain a mechanical stress index; S23, fusing the electrical stress index and the mechanical stress index serving as orthogonal components in a two-dimensional risk space through vector synthesis logic to generate systematic potential stress.
3. The rotary electric machine operation support method according to claim 1, wherein S3 specifically includes: s31, storing the systematic latent stress sequence calculated in real time into a time window buffer, and calculating the evolution rate by applying a difference method or a Kalman filtering algorithm to time sequence data; S32, utilizing the electrical stress index as a quantization index of the current harmonic interference intensity, and carrying out exponential adjustment on the reference boundary to generate a dynamic failure boundary.
4. A rotary electric machine operation support method according to claim 3, further comprising: And establishing a risk level evaluation rule based on the calculated systematic latent stress, the evolution rate and the dynamic failure boundary, and dividing the risk into a concerned level and an alarm level.
5. The rotary electric machine operation support method according to claim 1, wherein S4 specifically includes: s41, constructing a mode switching decision function, wherein the mode switching decision function synthesizes the normalized risk degree and the normalized risk rate to calculate a mode switching decision function value; s42, comparing the mode switching decision function value with a preset switching threshold value and a hysteresis threshold value to generate a control mode switching decision, when the mode switching decision function value exceeds the switching threshold value, generating a decision for switching to the survival optimal mode, when the mode switching decision function value is lower than the hysteresis threshold value, generating a decision for switching back to the performance optimal mode, and when the mode switching decision function value is between the switching threshold value and the hysteresis threshold value, maintaining the current control mode.
6. The rotary electric machine operation support method according to claim 1, wherein S5 specifically includes: s51, after switching to a survival optimal mode, an autoregressive prediction model which correlates the active power trimming quantity and the reactive power trimming quantity with future changes of systematic latent stress is built on line; S52, based on the autoregressive prediction model, aiming at minimizing the predicted systematic potential stress, and solving an inhibitory control strategy containing the optimal active power trimming quantity and the optimal reactive power trimming quantity.
7. The rotary electric machine operation support method according to claim 1, wherein S6 specifically includes: S61, collecting a new system state after the inhibitory control strategy is executed, and recalculating the real systematic latent stress as a feedback signal; s62, utilizing the feedback signal to correct the parameters of the autoregressive prediction model on line through a recursive least square algorithm.
8. A rotating electrical machine operation support system, based on the rotating electrical machine operation support method according to any one of claims 1 to 7, characterized by comprising the following modules: the data acquisition and preprocessing module is used for acquiring operation data of the rotating motor, wherein the operation data comprises three-phase current, terminal voltage and mechanical vibration acceleration, and harmonic voltage and harmonic current are extracted based on the operation data; The system comprises a latent stress estimation module, a system power generation module, a power generation module and a power generation module, wherein the latent stress estimation module is used for calculating an electrical stress index and a mechanical stress index, wherein the electrical stress index is determined based on harmonic voltage and harmonic current, and the mechanical stress index is determined based on mechanical vibration acceleration; The risk trend evaluation module is used for calculating the evolution rate of the systematic latent stress and generating a dynamic failure boundary based on the electrical stress index; The mode switching decision module is used for calculating a mode switching decision function value according to the systematic latent stress, the evolution rate and the dynamic failure boundary and generating a control mode switching decision based on the function value; The suppression strategy generation and execution module is used for responding to the control mode switching decision and generating and executing a suppression control strategy; the closed-loop correction module is used for collecting the operation data after the inhibitory control strategy is executed, and correcting the inhibitory control strategy in a closed-loop mode based on the updated operation data.

Description

Rotary motor operation support system and method thereof Technical Field The invention relates to the technical field of fault prediction and health management of rotating electric machines, in particular to a rotating electric machine operation support system and a rotating electric machine operation support method. Background The rotating motor is core equipment in the fields of industry and energy, the reliability and the safety of operation are of great importance, and under a complex operation environment, a rotating motor system, particularly a cluster system, can bear coupling pressure of multiple physical fields such as harmonic disturbance from a power grid, self structural vibration and the like. The interweaving action of the electric and mechanical stress can induce systematic instability risks such as subsynchronous oscillation and the like, and the traditional monitoring method is difficult to effectively cope with; In addition, the static threshold value cannot adapt to the dynamic change of the external working condition, so that the false alarm is easy to occur under severe working condition or the false alarm occurs under normal working condition, therefore, the prior art is often remained on a passive response level to faults, and is difficult to effectively early warn and actively avoid in the initial stage of systematic risk accumulation, thereby being incapable of effectively avoiding catastrophic equipment damage and large-scale shutdown accidents, and forming a potential threat to the operational reliability and safety of a key rotating motor system. Disclosure of Invention In order to solve the above technical problems, the present invention provides a rotary electric machine operation support system and a method thereof, specifically, the technical scheme of the present invention is as follows: a rotary electric machine operation support method comprising the steps of: s1, collecting operation data of a rotating motor, wherein the operation data comprise three-phase current, terminal voltage and mechanical vibration acceleration; S2, calculating an electrical stress index and a mechanical stress index, wherein the electrical stress index is determined based on harmonic voltage and harmonic current, and the mechanical stress index is determined based on mechanical vibration acceleration; S3, calculating the evolution rate of systematic latent stress, and generating a dynamic failure boundary based on an electrical stress index; s4, calculating a mode switching decision function value according to the systematic latent stress, the evolution rate and the dynamic failure boundary, and generating a control mode switching decision based on the function value; s5, generating and executing a suppression control strategy in response to the control mode switching decision; s6, collecting the operation data after executing the inhibitory control strategy, and correcting the inhibitory control strategy in a closed loop based on the updated operation data. Preferably, S2 specifically includes: S21, comparing the monitored subsynchronous harmonic power with the rated power of the motor, and carrying out normalization calculation to obtain an electrical stress index; s22, comparing the accumulated vibration energy in a period of time with a preset reference energy value, and carrying out normalization calculation to obtain a mechanical stress index; S23, fusing the electrical stress index and the mechanical stress index serving as orthogonal components in a two-dimensional risk space through vector synthesis logic to generate systematic potential stress. Preferably, S3 specifically includes: s31, storing the systematic latent stress sequence calculated in real time into a time window buffer, and calculating the evolution rate by applying a difference method or a Kalman filtering algorithm to time sequence data; S32, utilizing the electrical stress index as a quantization index of the current harmonic interference intensity, and carrying out exponential adjustment on the reference boundary to generate a dynamic failure boundary. Preferably, the method further comprises: And establishing a risk level evaluation rule based on the calculated systematic latent stress, the evolution rate and the dynamic failure boundary, and dividing the risk into a concerned level and an alarm level. Preferably, S4 specifically includes: s41, constructing a mode switching decision function, wherein the mode switching decision function synthesizes the normalized risk degree and the normalized risk rate to calculate a mode switching decision function value; s42, comparing the mode switching decision function value with a preset switching threshold value and a hysteresis threshold value to generate a control mode switching decision, when the mode switching decision function value exceeds the switching threshold value, generating a decision for switching to the survival optimal mode, when the mode switching decision funct